Marketing capabilities, technology capabilities and the financial performance of returnee firms in China

Jiangyong Lu* Department of Strategic Management

Guanghua School of ManagementPeking University

Beijing, China, 100871Tel: +86(0)10 62757913

Email: lujiangyong@gsm.pku.edu.cn

Xiaohui LiuSchool of Business and Economics

Loughborough UniversityLeicestershire LE11 3TUTel: + 44 (0)1509 223349

E-mail: X.Liu2@lboro.ac.uk

Seong-jin ChoiDepartment of Strategic Management

Guanghua School of ManagementPeking University

Beijing, China, 100871Tel: +86 15811017917

Email: neostar018@gmail.com

Abstract:

* Financial support from ESRC (RES-238-25-0027) is gratefully acknowledged.

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Adopting social capital theory and the capability-based view, this paper examines the

mechanisms through which returnees affect firm performance based on a sample of

high-tech returnee firms and non-returnee firms in China. We find that returnee firms

have a lower level of marketing capabilities and technology capabilities than their

local counterparts. We further link the perceived advantages and disadvantages of

returnee managers to technology capabilities and marketing capabilities. The findings

show that marketing capabilities and technology capabilities significantly mediate the

relationship between returnee managers and firm performance. The findings from the

study have both theoretical and practical implications.

Keywords: Returnee managers, firm performance, marketing capabilities, technology

capabilities, mediation.

Introduction

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In recent years, a large number of immigrants who were born in developing countries

but worked or were educated in developed countries have returned to their developing

home countries such as India and China.1 This emerging trend of international

migration has had important economic and social impact on both developing and

developed countries and thus has drawn the attention of public media and policy

makers. Governments in developing countries that have been worried for many years

about losing their best talent overseas as a result of ‘brain drain’, have welcomed the

‘brain return’ trend (Agrawal, Kapur, McHale, & Oettl, 2011; Mayr & Peri, 2009).

Some of them have even launched aggressive campaigns to lure their brightest minds

back to their home countries.2 As a result, think tanks in developed countries have

started reminding their governments that their countries are losing some of the world’s

brightest minds due to the reverse flow of migration between developing and

developed countries (Wadhwa, Saxenian, freeman, & Salkever, 2009).

The returnee phenomenon has also attracted increasing attention among

management scholars. The early literature in this area appraised the contributions of

returnee scientists and engineers with regard to transferring technological knowledge

from developed countries to developing countries (e.g., Saxenian, 2005). Later studies

examined the impact of returnees on indigenous innovation and technology spillovers

in developing countries (e.g., Filatotchev, Liu, Buck, & Wright, 2009; Wright, Liu,

Buck & Filatotchev, 2008). These studies emphasized returnee migrants’ advantages

with regard to the technological knowledge which they obtained when studying or

working in developed countries, but largely neglected any potential disadvantages that

returnees may have compared to their local counterparts. During their absence from

their home countries, returnees’ linkages with their home countries were very likely to

have been weakened, resulting in disadvantages in local networks compared with their

local counterparts who never left their home country. Consistent with social capital

theory (Adler & Kwon, 2002), several recent studies have observed that firms with

1 For example, according to official Chinese statistics updated to the end of 2012, 2.64 million students and scholars had gone abroad while 1.09 million had returned to China after they had completed their studies.2 For example, the Thousand Talents Program initiated by Chinese central government in 2008 offers top Chinese returnee scientists grants of 1 million RMB (about 146 thousand USD), high salaries, and generous lab funding (BusinessWeek, 2009).

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returnee leaders perform worse than other local firms, although the performance gap

between these two types of firms was narrowed in state controlled and older returnee

firms with stronger local networks (Li, Zhang, Li, Zhou, & Zhang, 2012; Obukhova,

Wang, & Li, 2012).

These most recent studies pointed out that returnee migrants lack local

connections and local knowledge compared to their local counterparts. However, no

study has analysed the specific mechanisms through which returnees’ advantages and

disadvantages affect firm performance. To address this gap, our study makes three

contributions to the existing literature on return migrants. First, we extend existing

studies by identifying the specific advantages and disadvantages associated with

returnees. More specifically, through comparing the technological and marketing

activities of returnee firms and non-returnee firms, we identify in what aspects

returnee firms have advantages and disadvantages. These aspects have been

overlooked in prior research which has implicitly assumed that returnees are able to

leverage their technological capability so that their firms outperform non-returnee

firms. However, the local context has been neglected. The findings from our research

will help deepen our understanding of how returnees affect firm performance when

they operate in the local context of their home country. Second, we move beyond the

existing literature by analysing the question of how returnees affect firm performance.

We find that returnees affect firm performance through influencing their firms’

marketing capabilities and technology capabilities. This helps address the puzzle of

why some returnee firms perform worse than non-returnee firms by going beyond

investigating when returnee firms perform worse (Li et al. 2012; Obukhova, Wang, &

Li, 2012). Third, the findings provide new insights into how to maximise the

advantages and minimise the disadvantages of returnees. In particular, a better

understanding of how returnee migrants affect firm performance will help returnees to

limit the effect of the disadvantages and strengthen the impact of the advantages by

adjusting the mechanisms through which their advantages and disadvantages affect

firm performance. Meanwhile, governments of developing countries may also benefit

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from this deeper understanding, and design suitable policies to help returnee migrants

overcome their disadvantages in their home countries.

This paper is structured as follows: Section 2 discusses the theoretical

background and hypotheses. Section 3 describes the data and methodology, while

Section 4 presents the empirical results. Section 5 discusses the findings and their

implications, followed by the conclusion in Section 6.

Theoretical Background and Hypotheses

Social capital is an important aspect of top-level managers based on their experiences

and attributes, and is built on interpersonal connections and the information,

influence, and solidarity derived from these connections (Adler & Kwon, 2002).

Given top-level managers’ key roles in firms, their social capital is an important

foundation to understand firms’ strategic choices and performance (Li & Zhang,

2007). Top-level managers play a more important role in small, young firms than in

large firms considering the relative small size of top executive teams in new ventures.

For example, studies on innovation strategy have found both top managers’ personal

characteristics and their social capital are important determinants of firms’ innovation

strategy (see a review in Crossan & Apaydin, 2010). In this paper, we compare the

impact of returnee top managers or CEOs with non-returnees on firm capabilities and

performance with emphasis on the social capital based on their backgrounds.

Top executives affect firm performance by making strategic decisions which may

help to ensure efficient use of firms’ resources. The capability-based view argues that

top managers play an important role in creating and maintaining firms’ capabilities

with regard to acquiring resources, shedding, integrating and recombining to generate

value through strategic decisions (Amit and Schoemaker, 1993; Felin and Foss, 2005;

Nelson & Winter, 1982). Marketing and technology strategies are among the most

fundamental strategic choices made by top managers as investment in developing and

commercialising new products is often the driver of future competitive advantage and

productivity. The role of top executives in making strategic choices about innovation

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and marketing can be particularly relevant in young, entrepreneurial high-tech firms

where the simplicity of the organizational structure and communication channels

allows top executives to interact with each other and with the firm’s resources to

influence innovation and marketing strategies (Rodenbach & Brettel, 2012). Thus, we

regard firms’ marketing capabilities and technology capabilities as the mechanism

through which returnee managers utilize their experience and social capital, such as

networks, to help their firms achieve performance goals.

We integrate social capital theory and the capability-based view to examine why

returnee firms perform differently from non-returnee firms in an emerging economy

with an emphasis on the mechanism through which returnee migrants affect firm

performance by shaping their firms’ marketing and technology capabilities. Top

managers’ social capital can be transformed into their firms’ capabilities because

managerial ties involve managers using their networks to exchange favours and

reciprocal obligations for organizational purposes (Peng & Luo, 2000). There are at

least three channels through which top managers’ social capital can benefit firm

capabilities. First, top managers’ social capital provides access to information through

a broad range of sources and with enhanced relevance, and timeliness (Peng & Luo,

2000). Second, influence, control, and power resulting from managers’ social capital

affect firms’ efficiency which allows firms to get things done effectively and achieve

their goals. Third, strong social norms and beliefs shared within managers’ networks

provide solidarity that encourages compliance and reduces the need for formal

controls, thus increasing effectiveness.

Recognising the importance of managers’ social capital as a determinant of

firms’ performance, a number of studies have focused on the contingency value of

managers’ social capital by studying the conditions under which managers’ social

capital benefits or hinders firm performance (Li & Zhang, 2007; Siegel, 2007).

Among returnee-related research, most existing studies have also focused on the

conditions under which the lack of social capital of returnee managers in their home

country has a large effect on firm performance (Li et al., 2012; Obukhova et al.,

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2012). Although it is important to understand when returnee managers’ social capital

matters to firm performance, it is also valuable to unpack how this matters because

individuals have limited ways to change institutions in which firms operate (e.g. state

controlling ownership in Li et al., 2012) and they cannot change their past experiences

(e.g. local alumni network in Obukhova et al., 2012). However, top managers have

many ways to change the strategies of their firms in order to improve firm

performance. Thus, studying the mechanisms through which the disadvantages of

returnees, i.e. the lack of social capital in their home country, affect firm performance

has important theoretical and practical implications.

It is recognised that the value of social capital lies in its appropriability and

convertibility into other forms of capital and capabilities (Adler & Kwon, 2002). In

other words, the value of cultivated social capital can only be realised through the

transformation of social capital into competences and capabilities that benefit firm

performance directly (Gu et al., 2008). Marketing capabilities and technology

commercialisation capabilities are two important capabilities firms need to achieve

good performance. The capability-based view suggests that it is the capabilities that

enable the deployment and leveraging of resources effectively that help to explain

why some firms perform better than others (Grant, 1996). Specifically, capabilities

enable firms to perform value-adding tasks more effectively and to create barriers to

imitation, enabling firms to enjoy sustainable advantage over their rivals. In sum,

capabilities are the key determinants of firms’ competitive advantage, and thus their

performance (Day, 1994; Verona and Ravasi, 2003; Zahra and Nielsen, 2002).

Social Capital, Marketing Capabilities and Financial Performance

Marketing capabilities are a multi-dimensional concept and can be defined as (1)

a firm’s ability to obtain resources that can be deployed into marketing activities

(input); (2) a firm’s ability to generate desired marketing objectives (output); and (3) a

firm’s ability to achieve more marketing objectives with fewer marketing inputs

(efficiency). Therefore, marketing capabilities represent a firm’s competences that

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help the firm reach customers, satisfy customers’ needs, and gain revenue from sales

(Vorhies and Morgan 2005). Marketing capabilities are believed to be one of the most

important capabilities that help firms to outperform their competitors, especially

during economic recessions (Srinivasan, Lilien, & Sridhar, 2011), in high-tech sectors

(Dutta, Narasimhan, & Rajiv, 1999) and in developing countries (Su, Peng, Shen, &

Xiao, 2013).

Given their importance for firm performance, researchers have revealed several

important antecedents to firms’ marketing capabilities, including resource slacks,

market orientation, and the social capital of managers. In particular, some studies have

revealed that social capital is a very important determinant of firms’ marketing

capabilities as it helps to access marketing resources and achieve superior marketing

performance in developing economies (Gu, Hung & Tse, 2008; Luo, Huang, & Wang,

2012). Managers’ social capital influences firm market capabilities by affecting the

information, process, and control necessary for efficient marketing activities. First,

marketing capabilities reflect firms’ market knowledge about customer needs and past

experiences in forecasting and responding to these needs (Day, 1994). Because

marketing capabilities are based on information and resources outside the firm (e.g.

information on competition or customers), and social capital enables firms to access

valuable knowledge and information on customer needs and market trends, the social

capital of managers can be a major antecedent of marketing capabilities by serving as

a bridge between firms and external information (Acquaah, 2007). Second, managers’

social capital, especially the trust relationship with customers and communities, helps

firms to increase the influence, control, and power of their marketing activities. For

example, Morgan & Hunt (1994) highlighted the role of shared norms and trust as a

major element of social capital that facilitates market exchanges. Third, solidarity

derived from social capital reduces opportunistic behaviour and the need for costly

monitoring of relationships with distributers (Adler & Kwon, 2002; Lin, 1999).

When returnees return to their developing home countries, they often face a

seemingly familiar yet different environment. Because of social, cultural, and

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institutional changes that occurred in their home countries when they were absent,

returnees may not have an accurate or comprehensive understanding of the local

market and society, or how to conduct business effectively in their home country (Li

et al., 2012). Recent studies have showed that returnees may find themselves facing

the ‘liability of foreignness’ when they re-enter their home countries after being away

for a long period of time (Szkudlarek, 2010; Obukhova et al., 2012). The ‘liability of

foreignness’ and lack of social capital in their home countries are very likely to limit

returnee firms’ marketing capabilities because marketing knowledge is typically

location-specific.

Recent studies have found that returnees tend to rely on their overseas networks

when they seek growth in overseas markets (Filatotchev et al., 2009), and the

dependence on overseas networks becomes more important when domestic

institutions are underdeveloped (Nanda & Khanna, 2010). These findings imply that

returnee managers are probably forced to rely on their overseas networks and overseas

markets because they lack local networks and find it difficult to overcome some

hurdles when exploring domestic markets. However, domestic markets are also very

important for returnee firms, given that the most important reason why returnees go

back to their home countries, like India and China, is to capture and exploit the

abundant opportunities within these emerging markets (Zweig et al., 2006). However,

without strong local social capital and a good understanding of local markets,

returnees may find it difficult to access customer information, establish trust

relationships with domestic distributers, and avoid opportunistic behaviour of

stakeholders in marketing activities. As a result, returnee managers’ disadvantages in

social capital may constrain their firms’ investment in marketing functions, raise the

cost of marketing activities, and increase the monitoring costs of marketing

relationships with stakeholders. Therefore, we propose that the presence of a returnee

manager affects firm financial performance indirectly through negatively influencing

firm marketing capabilities, in that

H1: The presence of returnee managers is negatively associated with firms marketing

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capabilities.

H2: Marketing capabilities mediate the impact of returnee managers on firm financial performance.

Social Capital, Technology Capabilities and Financial Performance

Technology capabilities refer to the processes that enable firms to invent new

technology and modify existing technology to develop new products and services.

Technology capabilities depend on the routines that help firms develop new

technological knowledge and produce new products. It is commonly accepted that

technology capabilities are key capabilities for those firms in high-tech sectors

seeking to achieve superior performance (Song, Droge, Hanvanch, & Calantone,

2005). Previous studies have revealed that internal and external technology resources

are critical for firms’ technology capabilities (Zahra & Nielsen, 2002). To access

broader sources of technological resources and to convert these resources into

technology capabilities, managers’ social capital may help firms to access information

on technology resources, to facilitate the control in technology asset exchanges, and to

reduce opportunistic activities in technology transactions. Empirical evidence has

shown that firms’ broader social capital resources are associated with superior

innovation orientation (Davidsson & Honig, 2003; Lee, Lee & Pennings. 2001).

Among various features of social capital, international experience was found to

be influential on a firm’s technology investment because top executives with

international experience possess more knowledge about foreign markets, more foreign

business practice experience, a broader worldview, and more professional ties with

overseas technology communities that help them to obtain advanced technology and

other resources needed for innovation activities (Rodenbach & Brettel, 2012). Most

returnees have acquired academic knowledge in the form of general education as well

as scientific and technical training in developed economies, and thus favour

innovation strategies because these developed economies are knowledge intensive and

emphasise growth through new products and innovation (Wright, et al., 2008). The

international vision and technological backgrounds of returnees may be reflected in a

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technology-focused managerial mindset that levers the competitive advantage of their

firms through boosting technology inputs. Thus, we argue that the special

characteristics of returnee managers may make them more innovation oriented and

lead them to invest more resources in innovation than their local counterparts.

Therefore, we propose

H3a: The presence of returnee managers is positively associated with firms’ technology input intensity.

Although innovation inputs and innovation outputs are interchangeably used in

existing studies on firm innovation, Hambrick & MacMillan (1985) challenged the

view of innovation as a deterministic process driven purely by technology investment.

Some studies have also found that the correlations between technology inputs and

innovative outputs range only between 0.20 and 0.50 (McLean & Round, 1978). From

another viewpoint, innovation can be regarded as a complex process involving the

adoption of internal or external technology inputs to generate technology outputs.

Thus, technology inputs, technology outputs and technology efficiency are interlinked

but different concepts. Specifically, technology inputs are defined as resources

dedicated to an innovation process (e.g. R&D expenditure, R&D employees);

technology outputs are outcomes derived from an innovation process (e.g. the number

of patents and new products), whereas technology efficiency is the production

function linking the technology inputs and the technology outputs of an innovation

process (Rodenbach & Brettel, 2012).

Emphasising only technology inputs has its drawbacks. Existing studies reveal

that innovation is a task fraught with high failure rates (Berggren & Nacher, 2001).

Additionally, studies investigating the innovation–performance relationship frequently

present mixed findings. Various empirical studies report that technology investment

does not influence firm performance (Birley & Westhead, 1990) or find negative

performance implications following technology investment (McGee et al., 1995).

These studies suggest that firms should be aware that knowing the importance of

technology investment and subsequently dedicating substantial resources to the

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innovation task might not be sufficient. Instead, firms should have capabilities to

manage the process of technology commercialisation, beginning with product

conception and concluding with effective product innovation and production (Zahra &

Nielsen, 2002). Emphasising technology output is important for the survival and

growth of small and new high-tech firms. High-tech start-ups usually have little

experience in the markets for which their innovations are most appropriate because of

their youth and small size,. For these firms, a key management challenge is how to

translate promising technology into new products and economic returns (Song, et al.,

2005).

A large percentage of returnees in high-tech start-ups had only studied/worked in

education institutes when they were abroad (Wright et al. 2008), which meant that

they lacked work experience and entrepreneurship experience, and this reduced the

relevance of their technological training with regard to technology commercialisation,

especially in the local environment of their home country. Taking a complementary

perspective, Wright et al. (2008) found that returnee entrepreneurs who acquired only

academic knowledge when they were abroad were more likely to choose a non-

university affiliated science park, in order to seek business experience. In the same

way, Zweig et al. (2006) found that returnees who brought back the latest

technologies in the world faced difficulties in commercialising their technology due to

their lack of experience in converting technology into new products.

Realising the disadvantages of returnees in the process of technology

commercialisation, especially in the business context of developing economies where

formal institutions are underdeveloped and local social networks are imperative

(Peng, & Zhou, 2005), we argue that although returnees are likely to champion

technology concepts, they may find it difficult to commercialise advanced technology.

In other words, return migrants may emphasise technology input but overlook

important dimensions, such as technology output and technology efficiency, which are

essential if they wish to capitalise on the value of innovation. Their weakness in

technology capabilities may be reflected in firm financial performance. Our

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discussion leads to the following hypotheses.

H3b: The presence of returnee managers is negatively associated with firms’ technology output intensity and technology efficiency.

H4: A firm’s technology capabilities mediate the impact of returnee managers on firm financial performance.

Data and Methodology

We tested our hypotheses with a data set of high-tech ventures in Zhongguancun

Science Park (ZSP) of Beijing, China. Established in 1988, ZSP is one of the largest

high-tech clusters in China within which local governments offer many preferential

policies, including tax reductions, facility and land use rights, and import privileges in

order to support the growth of ventures in high-tech industries (Zhang, Li, &

Schoonhoven, 2009). According to statistics regulations in China, all firms that are

identified as high-tech ventures must report their annual financial statements to the

Administrative Committees of these high-tech clusters. The data set of high-tech firms

in ZSP provides detailed information on the financial performance, human resource

management and R&D activities of these high-tech firms. The data set has recently

been used in studies of exporting, innovation, and the financial performance of high-

tech firms in China (e.g., Li et al., 2012; Todo et al. 2011). Similar to previous studies

using the same data set (Li et al. 2012; Todo et al. 2011), we focus on the sample

period 1996-2003. The reason for choosing year 2003 as the ending point is that the

reporting of financial data to the Administrative Committees of ZSP has been

voluntary since 2004, and only a small number of firms continued to report their

financial information. Thus, we use the data before 2004 in order to include as many

firms as possible in our analysis. Our sample consists of about 20,000 firm-year

observations for the period of 1996-2003.

Dependent variables

To test Hypothesis 1, we measure a firm’s marketing capabilities in three

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dimensions: marketing input intensity, marketing output intensity, and marketing

efficiency. We proxy a firm’s marketing input intensity with Sales Expenditure per

Employee which is logarithm transformed and represents the level of spending on

sales and marketing by a firm (Fang et al., 2011). We proxy a firm’s marketing output

intensity with Sales per Employee which is logarithm transformed and represents the

amount of revenue generated by each employee. We proxy a firm’s marketing

efficiency with Sales to Sales Expenditure Ratio which is also logarithm transformed

and represents how efficient a firm is in generating sales revenue from a unit of sales

expenditure (Morgan & Rego, 2009).

To test Hypotheses 3a and 3b, we also measure a firm’s technology capabilities

in three dimensions: technology input intensity, technology output intensity, and

technology efficiency. We proxy a firm’s technology input intensity with R&D

Expenditure per Employee which is logarithm transformed. R&D expenditure is the

total amount of R&D spending for a firm in a given year. We proxy a firm’s

technology output intensity using New Products per Employee which is also logarithm

transformed. New products are measured as the value of new products a firm

produces in a given year using new technology or a new design. The measure was

widely used as a proxy of technology output intensity in previous studies (e.g., David,

Hitt & Gimeno, 2001; Hambrik & MacMillan 1985; Lee, 2003). We proxy a firm’s

technology efficiency capability with New Products to R&D Expenditure Ratio. It

represents how many new product outputs a firm achieves given a unit of R&D

expenditure.

To test Hypotheses 2 and 4, we measure firm performance with return on assets

(ROA) and return on equity (ROE) which are defined as the percentage of net profit to

total assets of a firm and the percentage of net profit to total equity of a firm,

respectively (Robinson & McDougall, 2001).

Independent and control variables

Returnee Manager is a dummy variable which is coded as 1 if a firm’s legal

representative was a returnee and 0 otherwise (Li et al., 2012). In our sample, 9.5

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percent of firm-year observations had a returnee as the legal representative of the

firm.3

In analyses of returnee managers’ impacts on firms’ technology capabilities and

marketing capabilities, as well as firm performance, we control for individual-level,

firm-level, and industry-level variables that could affect these factors. To differentiate

the impact of returnee and non-returnee managers, we control for as many

characteristics of firm leaders as possible. Following Rodenbach & Brettel (2012), we

control for managers’ gender and education level, respectively. Manager Gender is a

dummy variable which equals 1 if the legal representative is male, and 0 otherwise.

Manager Education is measured as the number of years of formal education that a

CEO experienced. Following Hamori & Koyuncu (2011), we assume that CEOs who

hold a college diploma, bachelor degree, master degree, and PhD degree had 14, 16,

18, and 21 years of formal education, respectively.

At firm level, we also control for Firm Age and Firm Size, which are defined as

the natural logarithm of the number of years since a firm’s establishment and natural

logarithm of the number of employees of a firm, respectively. While some firms in

ZSP are privately owned, others retain some public ownership after being established

as spin-offs from public institutions or state-owned enterprises. Li et al. (2012) found

that public ownership significantly mitigates the disadvantages of returnee Legal

Representatives, and reduces the performance gap between returnee firms and non-

returnee firms. We control for potential differences between firms with different

ownership structure. Public Ownership is defined as a dummy variable which is

coded as 1 if a firm was registered as a public-owned or state-owned firm, and 0

otherwise. Foreign Ownership is defined as a dummy variable which is coded as 1 if a

firm was registered as a foreign investor owned firm, and 0 otherwise. In our sample,

17.6 percent of firm-year observations were registered as publicly owned companies.

Previous studies have also highlighted the important role of business groups in

3 China’s “General Principles of The Civil Law” (Article 38) defines a firm’s legal representative as the responsible person who acts on behalf of the firm in exercising its functions and power. China’s “Company Law” (Article 13) further clarifies that “The legal representative of a company may be represented by the chairman, executive director or manager of a company in compliance with its articles of association and registered in accordance with the law.” We define returnee firm in the same way as in Li et al. (2012), and use returnee managers and returnee legal representative interchangeably in the paper.

15

emerging markets (Khanna and Yafeh, 2007). To take this into account, we control for

Business Group Affiliation, which is coded as 1 if a firm was affiliated with a business

group in a year, and 0 otherwise.

At industry level, we control for competition effects within industries with

industrial concentration ratio measured by Herfindahl–Hirschman Index (HHI). We

also control for factors other than industrial innovation intensity, industrial marketing

intensity, and industrial concentration ratio with a set of 4-digit Technological Sector

Code dummies.

Results

Our data share a similar structure with that in Li et al. (2012). That is, one firm

could contribute multiple observations across years that were not independent from

each other. In this case, the random-effect panel model is the most appropriate for two

reasons. First, a fixed-effects approach requires variance in both dependent and

independent variables to assure that these variables are distinguishable from the fixed

effects (Judge et al., 1985). In our data, however, some variables, such as Returnee

Manager, are time-invariant and there is only a limited temporal variation during the

sample period. In contrast to fixed-effects models, random-effects models allow the

estimation of the impact of time-invariant variables. In addition, fixed-effects models

generate biased estimates for a short sample period like ours, whereas random-effects

models allow the derivation of efficient estimators that make use of both within and

between (group) variations and provide better estimates in this case (Heckman 1979).

Thus, we estimate random-effects specifications using xtreg in Stata version 11 in

analyses.

Table 1 presents descriptive statistics and correlations for the variables. First, the

variable of Returnee manager is negatively and significantly correlated with two

measures of firm performance – ROA and ROE, showing that returnee firms perform

worse than non-returnee firms. Second, all proxies of firm marketing capabilities

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(Sales expenditure per employee, Sales per employee, Sales to sales expenditure ratio)

and technology capabilities (R&D expenditure per employee, New products per

employee, New product to R&D expenditure ratio) are positively and significantly

correlated with ROA and ROE, reflecting the fact that firm capabilities are important

determinants of firm performance. Third, proxies of firm marketing capabilities are all

negatively and significantly correlated with Returnee manager. Meanwhile, among

proxies of firm technology capabilities, only the proxy of technology input intensity

(R&D expenditure per employee) is positively correlated with Returnee manager,

while the proxies of technology output intensity and technology efficiency (New

products per employee and New product to R&D expenditure ratio) are negatively

correlated with Returnee manager .

Table 2 reports the results of regressions testing the effects of returnees on firm

marketing and technology capabilities. The coefficients of returnee manager in

regressions on marketing input (Column 1), marketing output (Column 2), and

marketing intensity (Column 3) are all negative and statistically significant. The

results indicate that Returnee managers are negatively associated with firm marketing

capabilities and thus support Hypothesis 1. The coefficient of Returnee manager on

technology input (Column 4) is positive and statistically significant, showing that

returnees positively affect firm technology input, and thus supports Hypothesis 3a.

The coefficients of Returnee manager on technology output (Column 5) and

technology efficiency (Column 6) are all negative, but only statistically significant in

Column 6. The results show that returnee managers negatively affect firm technology

output and technology efficiency, and partially support Hypothesis 3b.

The results in Columns 4-6 in Table 2 show that returnee firms have higher

technology input intensity than non-returnee firms and are the same as non-returnee

firms in new product output, but they are worse than non-returnee firms in technology

efficiency. To further study the reasons behind such a pattern, we investigate the

structures of technology investment for returnee firms and non-returnee firms. Our

analyses show that returnee firms invest a larger proportion of their technology

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investment in basic R&D activities than non-returnee firms (p<0.01). Previous studies

revealed that basic scientific inventions are less likely to be commercialised than

applied inventions (Arrow, 1962). Thus, differences in the attributes of technology

inputs help to explain why returnee firms invest more in technology activities but

generate fewer new product outputs.

Although Hypotheses 1, 3a, and 3b are supported based on statistical

significance results, we check the economic significance of the Returnee manager

variable by calculating its Cohen’s d values and compare the values with benchmarks

set in Cohen (1998) and average values in existing international business (IB) and

management studies (Ellis, 2010). First, Columns 1-3 in Table 2 show that the

coefficients of Returnee manager are -0.076, -0.300, and -0.204, respectively. These

unstandardized coefficients imply that the mean of marketing capabilities for returnee

firms are/is? lower than that for non-returnee firms. Cohen’s d can be calculated by

dividing the mean with the standard deviation of the full sample and this generates the

effect size of -0.051 (d=-0.076/1.494), -0.152 (d=-0.300/1.974) and -0.083 (d=-

0.204/2.468), respectively. Similar calculations based on the coefficients of Returnee

manager in Columns 4-6 of Table 2 show that effect sizes of this variable on the

technology input, technology output, and technology efficiency of firms are 0.086

(d=0.098/1.143), -0.038 (d=-0.077/2.003) and -0.059 (d=-0.059/0.997), respectively.

These results show that returnee managers negatively affect the output and efficiency

of marketing and technology capabilities. Comparing the magnitudes of these impacts

indicates that the effect sizes of returnees’ negative impacts on marketing output and

efficiency are larger than that on technology output and efficiency (-0.152 vs. -0.038;

-0.083 vs. -0.059). According to Cohen’s (1988) recommended benchmarks, the effect

sizes we observed for returnee managers are less than half of the threshold for a small

class. However, some recent studies on the practice of effect size reporting in multiple

disciplines encourage scholars to compare effect size in their studies with findings in

the same field instead of a fixed benchmark as proposed in Cohen (1988) because

effect sizes may vary across disciplines (Aguinis, 2010; Ellis, 2010). Comparing the

18

average effect sizes in existing IB studies (Ellis, 2010) and psychology studies

(Aguinis, 2005), which are about half of the benchmark defined by Cohen (1988), we

propose that the impact of returnee managers on firm marketing and technology

capabilities is of similar economic significance as in other IB studies.

To test the mediation effects of marketing capabilities and technology

capabilities in the relationship between returnee managers and firm performance, we

follow Baron & Kenny’s (1986) four-step method. First, Column 1 and Column 5 in

Table 3 show that the coefficients of Returnee manager are negatively and

significantly associated with the ROA and ROE of the sample firms, respectively.

Second, the tests on Hypotheses 1, 3a, and 3b, reported above show that Returnee

manager is significantly associated with proxies of marketing capabilities and

technology capabilities (except for the proxy of technology output). As marketing

efficiency and technology efficiency are conceptually important and statistically

significant in the above tests, we focus on the mediating effects of marketing

efficiency and technology efficiency in the next steps. Third, in Table 3, we introduce

the proxy of marketing efficiency in Columns 2 and 6, the proxy of technology

efficiency in Columns 3 and 7, and both proxies in Columns 4 and 8. The results show

that, in Columns 2-4 and 6-8, the coefficients of Returnee manager become

statistically insignificant. Meanwhile, the coefficients of marketing efficiency and

technology efficiency proxies are statistically significant (except for the proxy of

marketing efficiency in Column 8). The results indicate that marketing efficiency and

technology efficiency fully mediate the relationship between Returnee manager and

firm performance, and the negative effect of Returnee manager on firm performance

is transmitted through the mediators, firm marketing efficiency and technology

efficiency. Fourth, we test the statistical significance of the mediators using Sobel’s

(1982) t-test, which shows that the mediating variables (marketing efficiency and

technology efficiency) carry the effect of the independent variable (returnee manager)

on the dependent variables (ROA and ROE). In sum, the results reported in Table 3

support Hypotheses 2 and 4 which state that a firm’s marketing and technology

19

capabilities in terms of efficiency mediate the impact of returnee managers on firm

financial performance.

Discussion

This paper studies how returnee managers affect firm performance using a data set of

high-tech firms in ZSP, the largest science park in China. Recent studies have shown

that returnee firms do not necessarily perform better than non-returnee firms (Li et al.,

2012; Obukhova et al., 2012). This paper confirms the negative impacts of returnee

managers on performance in previous studies using alternative performance proxies

(e.g. ROA and ROE).

Previous studies have argued that returnee firms are likely to enjoy technology

advantages due to returnee managers’ overseas technology background and

connection to overseas technology networks. However, advanced technology

knowledge and international technology networks do not necessarily generate more

commercial value or mean higher technology efficiency. We investigate in detail

whether returnee firms are better than non-returnee firms in three dimensions of

technology capabilities: technology input, technology output, and technology

efficiency. We find that returnee firms have higher technology input intensity

measured by R&D expenditure per employee. However, the technology

commercialisation capability of returnee firms, measured by new products per

employee, is not significantly different from that of non-returnee firms. Further, we

find that technology efficiency for returnee firms, measured by new products to R&D

expenditure ratio, is even lower than that for non-returnee firms. The findings imply

that returnee firms may not be able to benefit financially from their ‘technology

advantages’ because their technology advantages may be associated with high input,

low commercial value and the low efficiency of technology activities. In other words,

technology capabilities, which were usually thought of as advantages for returnee

firms, turn out to be disadvantageous.

Parallel to technology capabilities, we investigate the relationship between

20

returnee managers and marketing capabilities. We measure marketing capabilities in

three dimensions: marketing input, marketing output, and marketing efficiency. We

find that returnee firms are worse off in all three dimensions. Specially, returnee firms

put less input into marketing or are less market oriented, produce less output from

marketing activities, and have lower marketing efficiency than non-returnee firms. We

also find that the magnitude of the negative impact of returnee managers on marketing

efficiency is larger than that of returnees on firms’ technology efficiency. The finding

is consistent with the common belief that returnee firms’ major disadvantage lies in

their weak marketing capabilities (e.g., Wright et al., 2008).

To solve the puzzle of why returnee firms perform worse than non-returnee

firms, we go beyond existing studies by identifying the mechanisms through which

returnee managers affect firm performance. In particular, we link returnees’

advantages and disadvantages to marketing capabilities and technology capabilities.

We argue that returnee managers negatively affect the marketing orientation (input),

marketing achievement (output), and marketing efficiency of their firms due to the

lack of local social capital and knowledge of the local market. Meanwhile, although

returnee managers have better access to global sources of technology inputs, they do

not have advantages in commercialising technology in the local market comparing

with local counterparts. We find that firm marketing capabilities and technology

capabilities fully mediate the negative impact of returnee managers on firm

performance and hence we identify a clear mechanism through which returnee

managers influence firm performance.

This paper contributes to the existing literature in several ways. First, we go

beyond existing studies which argue that returnees have advantages and disadvantages

but do not identify what specific advantages and disadvantages returnees have. We

focus on technology capabilities and marketing capabilities which are most likely to

be associated with the advantage and disadvantage of returnee firms (Wright, et al.,

2008; Li et al., 2012). Our findings reveal that technology is not a realised advantage

of returnee firms because returnee firms lack technology commercialisation ability

21

and are less efficient in R&D. We also found that marketing capabilities represent a

true disadvantage or weakness of returnee firms as they are worse in marketing input,

marketing output, and marketing efficiency than non-returnee firms. Our findings

reveal the ‘dark side’ of innovation. Innovation orientation is a necessary condition,

but not sufficient to lead to a high level of firm performance as merely focusing on

innovation may hurt performance. Technology commercialisation capabilities and

marketing capabilities are more crucial to firm success than innovation itself. Hence,

our study broadens the concept of innovation and calls for more emphasis on

innovation-related activities, such as technology commercialisation and market

orientation, in innovation studies.

Second, we find that the negative impact of returnees on firm performance is

realised through affecting firm marketing and technology capabilities. As such, the

negative impact of returnees on firm performance totally disappears after taking firm

marketing and technology capabilities into account. The findings deepen our

understanding of how top executives in general and returnee managers in particular

affect firm performance by linking their characteristics to firm performance thorough

their role in building firm capabilities (Barker & Mueller, 2002). The mediating

effects found in the paper help us to unpack the ‘black box’ process underlying the

complex relationship between returnee managers’ characteristics and firm

performance.

Our study has important practical implications. First, policy makers who are

eager to attract more returnee talent to stimulate the economic and societal

development in developing countries like China and India should be aware that

returnee talent may not be able to bring short-term benefits to their home country on

the large scale expected. The apparent advantages returnees have over non-returnees

may not be materialise, given that the environments in developing countries are

different from those in the developed countries where returnees obtained their

‘advantages’. Second, returnees who are seeking opportunities in their developing

home countries should be aware of the difficulties they will face after their return.

22

Although the experience and knowledge they accumulated in developed countries will

be valuable in their home countries, it may take a lot of time and effort to realise the

advantages. In addition, although returnees mostly return from developed countries in

which intense competition is the industry norm, the business environments in their

developing home countries could be even more dynamic and turbulent and thus

neither their technological knowledge nor their market experience are guaranteed to

be competitive advantages for their firms. Third, both policy makes and returnees who

want to find solutions to improve returnee firm performance should focus on the

means of improving firm marketing capabilities and technology commercialisation

capabilities because these two types of capabilities are critical channels through which

returnees can affect firm performance.

Like other research, this paper also has limitations. First, as our analyses are

based on secondary data, we use objective measures to proxy technology capabilities

and marketing capabilities. Both objective measures and subjective measures of these

constructs have advantages and disadvantages (Fang et al., 2011). Future studies using

survey data can test whether the hypotheses in this paper hold with subjective

measures. Second, as the returnee firms were relatively new in the sample period, we

may miss the long-term effects of returnee managers’ capabilities on firm

performance and the dynamic aspects of such capabilities. That is, although returnee

firms’ higher level of technology input causes a low level of technology efficiency

and firm performance, the impacts may be reversed in the long-term after returnee

firm managers become more adept at responding to the local technology

commercialisation environment. Meanwhile, marketing capabilities of returnee firms

are also likely to be improved as returnee managers adapt to the local marketing

environment. Thus, further studies should investigate whether the disadvantages of

returnee firms will turn into advantages over a longer period of time.

Conclusion

This paper investigates whether technological capabilities and marketing

23

capabilities mediate the impact of returnee managers on the firm performance of

Chinese high-tech firms in ZSP. We link perceived advantages and disadvantages of

returnee managers to technology capabilities and marketing capabilities. The findings

reveal that returnee firms have worse marketing capabilities and technology efficiency

than their counterparts. Meanwhile, the negative impact of returnee managers on firm

performance disappears after controlling for firm marketing capabilities and

technology capabilities, revealing that improving marketing capabilities and

technology capabilities can mitigate disadvantages associated with returnee firms.

This study is one of the first to delineate performance differences between returnee

firms and non-returnee firms and provide new insights into the returnee phenomenon

in developing countries. Our empirical evidence calls for more studies on returnee

firms when more fine-grained measures and a longer sample period are available.

24

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28

Table 1: Summary statistics and correlations of variables

　 　 Mean

S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 ROA 0.007

0.203

2 ROE 0.004

0.319

0.878

3 Returnee entrepreneur 0.095

0.293

-0.05

1

-0.04

9

4 Sales expenditure per employee

1.539

1.494

0.080

0.081

-0.02

6

5 Sales per employee 3.891

1.974

0.395

0.379

-0.09

30.50

4

6 Sales to sales expenditure ratio

3.319

2.468

0.270

0.254

-0.05

6

-0.46

40.46

1

7 R&D expenditure per employee

2.704

1.143

0.087

0.084

0.064

0.212

0.313

0.104

8 New products per employee

1.203

2.003

0.134

0.139

-0.03

50.23

80.31

80.04

30.08

6

9 New product to R&D expenditure ratio

0.536

0.997

0.123

0.127

-0.04

80.18

90.26

40.04

1-

0.111

0.901

10 Entrepreneur education 16.6

702.006

-0.01

6

-0.01

60.34

70.03

1-

0.008

-0.02

80.12

3-

0.020

-0.04

911 Entrepreneur gender 0.86

20.345

0.003

0.002

0.015

0.036

0.038

0.012

0.022

0.029

0.031

0.120

29

12 Firm age 4.57

93.929

0.054

0.054

-0.08

40.06

70.22

50.16

0-

0.043

0.185

0.197

-0.07

20.06

2

13 Firm size 2.97

70.999

0.151

0.154

-0.03

60.33

00.34

30.07

00.09

10.22

20.20

80.05

60.08

30.217

14 Foreign ownership 0.09

40.292

-0.06

9

-0.06

60.09

30.12

30.04

8-

0.054

0.099

0.071

0.061

0.093

0.048

0.027

0.086

15 Public ownership 0.17

60.381

0.012

0.019

-0.05

3

-0.03

80.07

20.12

5-

0.048

0.040

0.052

-0.03

70.07

50.380

0.128

-0.08

416

Business group affiliation

0.066

0.249

0.006

0.014

-0.02

40.07

50.08

90.02

40.04

60.06

10.04

60.00

60.04

40.140

0.194

0.021

0.152

17 HHI 0.08

50.094

0.006

0.013

-0.01

3

-0.01

60.01

40.02

1-

0.046

0.090

0.091

-0.03

4

-0.01

30.069

0.004

-0.01

60.046

0.016

Note: n=19722; p<0.10=0.012, p<0.05=0.015, p<0.01=0.021.

30

Table 2: Impacts of returnee entrepreneur on marketing and technology input, output, and efficiency

　 Marketing

input

Marketing

output

Marketing

efficiency

　Technology

input

Technology

output

Technology

efficiency

Sales expenditure per employe

e

Sales per

employee

Sales to sales

expenditure ratio

R&D expenditure per

employee

New products

per employe

e

New product to R&D

expenditure ratio

(1) (2) (3) (4) (5) (6)Returnee manager -0.076* -

0.300** -0.204** 0.098** -0.077 -0.059*(0.037) (0.051) (0.066) (0.032) (0.052) (0.026)

Manager education 0.005 -0.001 -0.008 0.042** -0.010 -0.016**

(0.006) (0.008) (0.010) (0.005) (0.008) (0.004)Manager gender 0.040 0.027 -0.013 0.028 0.031 0.027

(0.033) (0.046) (0.059) (0.028) (0.044) (0.022)Firm age 0.017** 0.096** 0.084** 0.001 0.060** 0.031**

(0.003) (0.005) (0.006) (0.003) (0.004) (0.002)Firm size 0.380** 0.492** 0.134** 0.045** 0.362** 0.174**

(0.012) (0.016) (0.021) (0.010) (0.016) (0.008)Foreign ownership 0.479** 0.157** -0.458** 0.334** 0.371** 0.167**

(0.042) (0.057) (0.073) (0.034) (0.053) (0.027)Public ownership -0.097** 0.019 0.254** -0.005 -0.158** -0.067**

(0.028) (0.039) (0.051) (0.025) (0.041) (0.021)Business group affiliation 0.030 0.083 0.014 0.109** 0.063 -0.008

(0.037) (0.052) (0.069) (0.034) (0.057) (0.028)HHI -0.182 -0.425† -0.078 -0.111 0.084 0.067

(0.159) (0.226) (0.301) (0.152) (0.263) (0.130)Industry dummies Y Y Y Y Y YYear dummy Y Y Y Y Y YConstant 0.413** 2.478** 2.831** 1.935** 0.135 0.136†

(0.114) (0.159) (0.207) (0.100) (0.164) (0.082)chi2 1951.76

32625.04

1 668.086 887.764 2111.375

2081.811

N 19722 19722 19722 　 19722 19722 19722Note: † p<0.1, * p<0.05, ** p<0.01; Standard errors are in parentheses.

31

Table 3: Impacts of returnee managers on financial performance, and mediation effects of marketing efficiency and technology efficiency

　 　 ROA 　 ROE(1) (2) (3) (4) 　 (5) (6) (7) (8)

Manager education -0.001 0.000 0.003

† 0.001 -0.001 0.000 0.004 0.002

(0.001)

(0.001)

(0.002)

(0.001)

(0.001)

(0.001)

(0.003)

(0.002)

Manager gender -0.004

-0.003

-0.010

†-

0.006-

0.008-

0.006-

0.017†

-0.011

(0.005)

(0.005)

(0.006)

(0.005)

(0.008)

(0.008)

(0.009)

(0.008)

Firm age 0.001**

-0.006

†

-0.006

†

-0.006

†0.001

†-

0.010†

-0.009

†

-0.010

†0.000 (0.00

3)(0.00

3)(0.00

3)(0.00

1)(0.00

5)(0.00

5)(0.00

5)Firm size 0.032

**0.020

**-

0.007 0.007 0.051**

0.033**

-0.008 0.012

(0.002)

(0.005)

(0.016)

(0.010)

(0.003)

(0.009)

(0.026)

(0.016)

Foreign ownership-

0.057**

-0.014

-0.095

**

-0.053

**

-0.083

**-

0.019-

0.141**

-0.080

**(0.00

6)(0.01

9)(0.01

7)(0.01

5)(0.00

9)(0.03

0)(0.02

7)(0.02

4)

Public ownership-

0.013**

-0.037

**0.005

-0.016

†

-0.013

†

-0.048

**0.014 -

0.017(0.00

5)(0.01

1)(0.00

9)(0.00

9)(0.00

7)(0.01

7)(0.01

4)(0.01

4)Business group affiliation

-0.014

*

-0.015

*

-0.012

†

-0.014

*-

0.012-

0.014-

0.009-

0.012(0.00

6)(0.00

6)(0.00

6)(0.00

6)(0.01

0)(0.01

0)(0.01

0)(0.01

0)HHI 0.029 0.036 0.024 0.030 0.086

†0.097

*0.078

†0.087

†(0.02

9)(0.02

9)(0.02

9)(0.02

9)(0.04

6)(0.04

6)(0.04

6)(0.04

6)Industry dummies Y Y Y Y Y Y Y YYear dummy Y Y Y Y Y Y Y Y

Returnee manager-

0.020**

-0.001

-0.005

-0.003

-0.028

**0.001 -

0.006-

0.003(0.00

6)(0.01

0)(0.00

8)(0.00

9)(0.00

9)(0.01

5)(0.01

3)(0.01

5)Sales to sales expenditure ratio(Marketing efficiency)

0.093*

0.048†

0.139* 0.070

(0.039)

(0.029)

(0.061)

(0.046)

New product to R&D expenditure ratio

0.236*

0.114*

0.352*

0.175*

32

(Technology efficiency)(0.09

8)(0.05

0)(0.15

5)(0.08

0)

Constant-

0.075**

-0.339

**

-0.113

**

-0.230

**

-0.126

**

-0.519

**

-0.183

**

-0.352

**(0.01

9)(0.10

9)(0.02

4)(0.08

5)(0.02

9)(0.17

4)(0.03

7)(0.13

5)chi2 574.9

24574.9

24574.9

24574.9

24565.6

99565.6

99565.6

99565.6

99N 1972

21972

21972

21972

2 　 19722

19722

19722

19722

Note: † p<0.1, * p<0.05, ** p<0.01; Standard errors are in parentheses.

33